Data Scientist
GARGI TECHNOLOGIES INC
yesterday
Role details
Contract type
Permanent contract Employment type
Full-time (> 32 hours) Working hours
Regular working hours Languages
English Experience level
IntermediateJob location
Tech stack
Amazon Web Services (AWS)
Data analysis
Computer Vision
Azure
Big Data
Cloud Computing
Data Visualization
Hadoop
Statistical Hypothesis Testing
Python
Machine Learning
Natural Language Processing
Power BI
TensorFlow
SQL Databases
Tableau
Unstructured Data
Google Cloud Platform
Feature Engineering
PyTorch
Spark
Deep Learning
Generative AI
Scikit Learn
Information Technology
XGBoost
Machine Learning Operations
Looker Analytics
Data Pipelines
Databricks
Job description
- Analyze large and complex datasets to identify trends, patterns, and business opportunities.
- Build, validate, and deploy machine learning models for predictive and prescriptive analytics.
- Develop data pipelines and workflows to support data collection, processing, and reporting.
- Collaborate with cross-functional teams including engineering, product, and business stakeholders.
- Create dashboards, visualizations, and reports to communicate findings effectively.
- Perform exploratory data analysis (EDA) and feature engineering.
- Monitor model performance and recommend improvements as needed.
Requirements
- Bachelor''s or Master''s degree in Data Science, Computer Science, Statistics, Mathematics, Engineering, or a related field.
- 2+ years of experience in Data Science, Machine Learning, or Analytics roles.
- Strong proficiency in Python and SQL.
- Experience with Machine Learning frameworks such as Scikit-learn, TensorFlow, PyTorch, or XGBoost.
- Solid understanding of statistics, probability, hypothesis testing, and predictive modeling.
- Experience working with data visualization tools such as Tableau, Power BI, or Looker.
- Knowledge of cloud platforms such as AWS, Azure, or Google Cloud Platform (Google Cloud Platform).
- Experience handling structured and unstructured datasets.
Preferred Qualifications
- Experience with NLP, Deep Learning, Computer Vision, or Generative AI projects.
- Familiarity with big data technologies such as Spark, Hadoop, or Databricks.
- Experience deploying machine learning models in production environments.
- Knowledge of MLOps practices and tools.